DHANARAJ S dhanarajappu456

`$ whoami`

class Dhanaraj:
    title       = "Result-Driven · Event-Driven Engineer · Published NLP Researcher"

    identity    = [
        "I don't write code — I engineer outcomes.",
        "Every system I build is async, reactive, built to survive at scale.",
        "Events flow. Services decouple. Results ship.",
        "I research what I build — and build what I research.",
    ]

    published_at = "Dublin City University — MSc Computing (Secure Software Eng.)"
    research     = ["NLP", "Semantic Clustering", "LLMs", "RAG", "Agentic AI"]
    architecture = ["Event-Driven", "Microservices", "CQRS", "Saga", "Kafka"]
    stack        = ["Java", "Python", "Go", "React", "React Native", "Node.js"]
    devops       = ["Kubernetes", "Terraform", "GitOps", "Prometheus"]
    targeting    = ["Meta", "Amazon", "Apple", "Netflix", "Google"]
    philosophy   = "Ship for impact. Design for failure. Scale without mercy."

⚡ Result-Driven. Event-Driven. Research-Backed. ⚡

Every service reacts. Every system scales. Every paper contributes.

🛠️ Full Skill Arsenal

💻 Languages

🎨 Frontend & Mobile

⚙️ Backend & APIs

🌐 Distributed Systems & Event-Driven Architecture

🗄️ Databases & Storage

☸️ DevOps, Cloud & Infrastructure

🧪 Testing & Automation

🧠 AI / ML & NLP — Research-Grade

📊 Competitive Programming & GitHub Analytics

⚔️ Active on: LeetCode · TopCoder · Codeforces · HackerRank 🎯 Strongest areas: Dynamic Programming · Graph Algorithms · Segment Trees · Binary Search · System Design

🚀 Flagship Projects

Five production-grade systems. Each one event-driven. Each one solving a real problem.

Each one built the way MAANG engineers build.

⚡ 01 — Distributed Rate Limiter

- Problem  → Unprotected APIs get hammered. Abuse, DDoS, runaway clients destroy infra.
+ Solution → Distributed, Redis-backed rate limiter. Decision made in < 1ms. Every time.

📈 Result	💥 Impact
100k+ req/sec throughput	Sub-millisecond decision latency
Zero downtime under Redis node failure	Automatic cluster failover
Pluggable algorithm engine	Token bucket · Sliding window · Fixed window

🔄 Event-driven core: rate.limit.event → Kafka → analytics consumer → Grafana real-time abuse dashboard

Java Spring Boot Redis Cluster Kafka Docker Kubernetes Prometheus Grafana

🎨 02 — Sorting Algorithm Visualiser

- Problem  → Algorithms are invisible. Reading quicksort a hundred times ≠ understanding it.
+ Solution → Make every comparison, swap, and partition visible in real time.

📈 Result	💥 Impact
6+ algorithms with step-through mode	Live Big O complexity panel
Swap & comparison counters	Quantify algorithm cost in real time
Actively used as teaching tool	DSA prep for engineers

React TypeScript Web Animations API Vite

🤖 03 — JobPilot — Autonomous Job Application Engine

- Problem  → Applying to 50 jobs takes 50 hours of copy-paste. No engineer should do that.
+ Solution → AI agent that finds, tailors, and applies — while you sleep.

📈 Result	💥 Impact
Fully autonomous end-to-end	Zero manual intervention per application
LLM + RAG pipeline	Resume tailored per job description
Runs 24/7 unattended	Self-healing on Kubernetes

🔄 Event-driven core: scraper event → Kafka → relevance scorer → LLM tailoring worker → submission service → status event → React dashboard

Python LangChain OpenAI API RAG Playwright Selenium React Kafka Kubernetes

🔔 04 — JobAlert — Real-Time Push Notification Job Board

- Problem  → Refreshing job boards 20 times a day. Missing the perfect role hours after it posts.
+ Solution → Push-based alert system. The job board comes to you — instantly.

📈 Result	💥 Impact
< 30 second latency	Job posting → push notification
Multi-platform delivery	Telegram · Slack · WhatsApp · Discord
Intelligent deduplication	Zero spam, only relevant alerts

🔄 Event-driven core: job.posted → Kafka → filter service → notification dispatcher → platform webhook → delivery receipt → DLQ retry on failure

Python Kafka Redis FastAPI Telegram Bot API Slack API Webhooks Docker Kubernetes

🧠 05 — InfluenceIQ — AI Influencer-Follower Interaction Platform

- Problem  → Influencers drown in thousands of DMs. Followers get ghosted. Engagement collapses.
+ Solution → Research-backed AI platform. Reads every message. Responds like the influencer.

🎓 Production implementation of our peer-reviewed published paper — grounded in academic research and real-world validation.

📈 Research Result	💥 Significance
63.93% clustering accuracy on Quora Question Pairs	Zero supervision, zero labelled data
Sentence-BERT beat TF-IDF baseline	Semantic paraphrase detection
HDBSCAN dynamic adaptation	No predefined cluster count needed

🔄 Event-driven core: message.received → Sentence-BERT encoder → HDBSCAN cluster → RAG retrieval → LLM reply → confidence score → auto-send / human-review queue → reply.sent → analytics pipeline

Python Sentence-BERT HDBSCAN LangChain RAG OpenAI API FastAPI React PostgreSQL Redis Kafka Docker

📜 Published Research

Peer-reviewed, published, and cited — engineering backed by academic rigour.

🧬 Transforming Influencer-Follower Engagement with AI-Powered Response Systems Using Natural Language Processing

Dhanaraj Sujatha · Dhanushkumar S G 🏛️ Dublin City University — MSc Computing, Secure Software Engineering · July 2025

Abstract: Social media influencers receive hundreds of repetitive follower questions daily — manually responding is unsustainable. This paper presents a semi-automated Q&A pipeline that solves the scalability problem while preserving authentic, personalised engagement at scale. Three-stage modular architecture:

🔹 Stage 1 — Semantic Question Clustering: Questions encoded via Sentence-BERT (all-MiniLM-L6-v2) and grouped with HDBSCAN — achieving 63.93% clustering accuracy across 10 test sets on Quora Question Pairs, with zero supervision or labelled training data.
🔹 Stage 2 — Merged Question Generation: GPT-3.5-turbo with few-shot prompting consolidates each cluster into one coherent representative question — reducing influencer cognitive load without losing follower intent.
🔹 Stage 3 — Personalised Reply via RAG: The influencer's single answer is stored in a persistent knowledge base. Future similar questions trigger retrieval and LLM-crafted on-brand personalised replies.

Key contributions: Modular NLP pipeline · Sentence-BERT embeddings · HDBSCAN density clustering · RAG knowledge retrieval · LLM prompt engineering · Human-in-the-loop design

Python Sentence-BERT HDBSCAN OpenAI API RAG Django React

🔥 What I'm Grinding Right Now

current_missions:
  InfluenceIQ:  "Extending published research → multi-agent orchestration + MCP persistent memory"
  JobPlatform:  "Merging JobPilot + JobAlert → one unified autonomous job-hunting engine"
  Distributed:  "Raft · Paxos · Vector Clocks · CRDTs · Exactly-once semantics"
  Scale:        "100M users · Petabyte event logs · Five-nines availability"
  DSA:          "LeetCode Hard + contests — consistent, relentless, every single day"

Open to senior engineering roles at Meta · Amazon · Apple · Netflix · Google

Navigation Menu